Research Trends Code
This is the code and bibtex files for the paper Research trends in Coquerel’s sifaka (Propithecus coquereli)
#Descriptive analysis
results <- biblioAnalysis(M, sep = ";")
S <- summary(object = results, k = 10, pause = FALSE)##
##
## MAIN INFORMATION ABOUT DATA
##
## Timespan 1978 : 2022
## Sources (Journals, Books, etc) 35
## Documents 92
## Average years from publication 10.1
## Average citations per documents 13.79
## Average citations per year per doc 1.229
## References 3440
##
## DOCUMENT TYPES
## article 70
## article; proceedings paper 1
## editorial material 1
## meeting abstract 13
## note 2
## proceedings paper 1
## review 4
##
## DOCUMENT CONTENTS
## Keywords Plus (ID) 379
## Author's Keywords (DE) 269
##
## AUTHORS
## Authors 281
## Author Appearances 350
## Authors of single-authored documents 8
## Authors of multi-authored documents 273
##
## AUTHORS COLLABORATION
## Single-authored documents 8
## Documents per Author 0.327
## Authors per Document 3.05
## Co-Authors per Documents 3.8
## Collaboration Index 3.25
##
##
## Annual Scientific Production
##
## Year Articles
## 1978 1
## 1983 1
## 1991 1
## 1992 2
## 1993 1
## 1995 1
## 1999 1
## 2000 1
## 2001 2
## 2002 1
## 2003 1
## 2004 3
## 2005 2
## 2006 2
## 2007 3
## 2008 1
## 2009 3
## 2010 2
## 2011 3
## 2012 6
## 2013 1
## 2014 9
## 2015 3
## 2016 9
## 2017 3
## 2018 8
## 2019 3
## 2020 8
## 2021 8
## 2022 2
##
## Annual Percentage Growth Rate 1.587808
##
##
## Most Productive Authors
##
## Authors Articles Authors Articles Fractionalized
## 1 GREENE LK 6 LEHMAN SM 3.33
## 2 LEHMAN SM 6 ROSS AC 2.00
## 3 GANZHORN JU 5 GANZHORN JU 1.62
## 4 YODER AD 5 GREENE LK 1.36
## 5 DREA CM 4 GARDNER CJ 1.33
## 6 GUEVARA EE 4 MCGOOGAN KC 1.33
## 7 CAMPBELL JL 3 DREA CM 1.31
## 8 DONATI G 3 GRIESER B 1.00
## 9 EISEMANN JH 3 MEADOR LM 1.00
## 10 EPPLEY TM 3 NUNN CL 1.00
##
##
## Top manuscripts per citations
##
## Paper DOI TC TCperYear NTC
## 1 SCORDATO ES, 2007, CHEM SENSES 10.1093/chemse/bjm018 83 5.19 2.62
## 2 RAVOSA MJ, 1993, AM J PHYS ANTHROPOL 10.1002/ajpa.1330920408 75 2.50 1.00
## 3 CAMPBELL JL, 2000, AM J PRIMATOL 10.1002/1098-2345(200011)52:3<133::AID-AJP2>3.0.CO;2-\\# 66 2.87 1.00
## 4 JACOBS GH, 2002, VISION RES 10.1016/S0042-6989(01)00264-4 55 2.62 1.00
## 5 GODFREY LR, 2012, AM J PHYS ANTHROPOL 10.1002/ajpa.21615 55 5.00 2.80
## 6 DA SILVA AJ, 2003, VET PARASITOL 10.1016/S0304-4017(02)00384-9 49 2.45 1.00
## 7 HAYES RA, 2004, AM J PRIMATOL 10.1002/ajp.20038 41 2.16 1.11
## 8 MAYOR MI, 2004, INT J PRIMATOL 10.1023/B:IJOP.0000029127.31190.e9 39 2.05 1.05
## 9 MCKENNEY EA, 2015, PLOS ONE 10.1371/journal.pone.0124618 39 4.88 2.49
## 10 PASTORINI J, 2001, AM J PRIMATOL 10.1002/1098-2345(200101)53:1<1::AID-AJP1>3.0.CO;2-J 38 1.73 1.49
##
##
## Corresponding Author's Countries
##
## Country Articles Freq SCP MCP MCP_Ratio
## 1 USA 51 0.6456 38 13 0.255
## 2 CANADA 5 0.0633 3 2 0.400
## 3 GERMANY 5 0.0633 1 4 0.800
## 4 UNITED KINGDOM 5 0.0633 3 2 0.400
## 5 FRANCE 4 0.0506 0 4 1.000
## 6 AUSTRALIA 3 0.0380 1 2 0.667
## 7 CHINA 2 0.0253 2 0 0.000
## 8 JAPAN 2 0.0253 0 2 1.000
## 9 PORTUGAL 2 0.0253 0 2 1.000
##
##
## SCP: Single Country Publications
##
## MCP: Multiple Country Publications
##
##
## Total Citations per Country
##
## Country Total Citations Average Article Citations
## 1 USA 938 18.4
## 2 GERMANY 62 12.4
## 3 AUSTRALIA 53 17.7
## 4 CANADA 51 10.2
## 5 FRANCE 51 12.8
## 6 JAPAN 43 21.5
## 7 UNITED KINGDOM 39 7.8
## 8 PORTUGAL 32 16.0
## 9 CHINA 0 0.0
##
##
## Most Relevant Sources
##
## Sources Articles
## 1 AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 15
## 2 AMERICAN JOURNAL OF PRIMATOLOGY 13
## 3 INTERNATIONAL JOURNAL OF PRIMATOLOGY 12
## 4 PLOS ONE 5
## 5 PRIMATES 5
## 6 JOURNAL OF ZOO AND WILDLIFE MEDICINE 4
## 7 ZOO BIOLOGY 4
## 8 FOLIA PRIMATOLOGICA 3
## 9 ANATOMICAL RECORD-ADVANCES IN INTEGRATIVE ANATOMY AND EVOLUTIONARY BIOLOGY 2
## 10 ANIMAL BEHAVIOUR 2
##
##
## Most Relevant Keywords
##
## Author Keywords (DE) Articles Keywords-Plus (ID) Articles
## 1 PROPITHECUS 14 MADAGASCAR 14
## 2 MADAGASCAR 10 PROPITHECUS-VERREAUXI-COQUERELI 12
## 3 LEMUR 9 HAPALEMUR-GRISEUS 11
## 4 COQUEREL'S SIFAKA 7 VARECIA-VARIEGATA 11
## 5 LEMURS 7 BEHAVIOR 10
## 6 PRIMATES 7 EVOLUTION 10
## 7 PROPITHECUS COQUERELI 7 PRIMATES 10
## 8 PROSIMIAN 5 FOREST 7
## 9 SIFAKA 4 PRIMATE 7
## 10 STREPSIRRHINES 4 RING-TAILED LEMURS 6knitr::kable(S$MainInformationDF, caption = "Summary Information") #main information| Description | Results |
|---|---|
| MAIN INFORMATION ABOUT DATA | |
| Timespan | 1978:2022 |
| Sources (Journals, Books, etc) | 35 |
| Documents | 92 |
| Average years from publication | 10.1 |
| Average citations per documents | 13.79 |
| Average citations per year per doc | 1.229 |
| References | 3440 |
| DOCUMENT TYPES | |
| article | 70 |
| article; proceedings paper | 1 |
| editorial material | 1 |
| meeting abstract | 13 |
| note | 2 |
| proceedings paper | 1 |
| review | 4 |
| DOCUMENT CONTENTS | |
| Keywords Plus (ID) | 379 |
| Author’s Keywords (DE) | 269 |
| AUTHORS | |
| Authors | 281 |
| Author Appearances | 350 |
| Authors of single-authored documents | 8 |
| Authors of multi-authored documents | 273 |
| AUTHORS COLLABORATION | |
| Single-authored documents | 8 |
| Documents per Author | 0.327 |
| Authors per Document | 3.05 |
| Co-Authors per Documents | 3.8 |
| Collaboration Index | 3.25 |
knitr::kable(S$MostProdAuthors, caption = "Most Productive Authors") #Most productive Authors| Authors | Articles | Authors | Articles Fractionalized |
|---|---|---|---|
| GREENE LK | 6 | LEHMAN SM | 3.33 |
| LEHMAN SM | 6 | ROSS AC | 2.00 |
| GANZHORN JU | 5 | GANZHORN JU | 1.62 |
| YODER AD | 5 | GREENE LK | 1.36 |
| DREA CM | 4 | GARDNER CJ | 1.33 |
| GUEVARA EE | 4 | MCGOOGAN KC | 1.33 |
| CAMPBELL JL | 3 | DREA CM | 1.31 |
| DONATI G | 3 | GRIESER B | 1.00 |
| EISEMANN JH | 3 | MEADOR LM | 1.00 |
| EPPLEY TM | 3 | NUNN CL | 1.00 |
knitr::kable(S$MostCitedPapers, caption = "Most Cited Papers") #most cited paper| Paper | DOI | TC | TCperYear | NTC |
|---|---|---|---|---|
| SCORDATO ES, 2007, CHEM SENSES | 10.1093/chemse/bjm018 | 83 | 5.19 | 2.62 |
| RAVOSA MJ, 1993, AM J PHYS ANTHROPOL | 10.1002/ajpa.1330920408 | 75 | 2.50 | 1.00 |
| CAMPBELL JL, 2000, AM J PRIMATOL | 10.1002/1098-2345(200011)52:3<133::AID-AJP2>3.0.CO;2-# | 66 | 2.87 | 1.00 |
| JACOBS GH, 2002, VISION RES | 10.1016/S0042-6989(01)00264-4 | 55 | 2.62 | 1.00 |
| GODFREY LR, 2012, AM J PHYS ANTHROPOL | 10.1002/ajpa.21615 | 55 | 5.00 | 2.80 |
| DA SILVA AJ, 2003, VET PARASITOL | 10.1016/S0304-4017(02)00384-9 | 49 | 2.45 | 1.00 |
| HAYES RA, 2004, AM J PRIMATOL | 10.1002/ajp.20038 | 41 | 2.16 | 1.11 |
| MAYOR MI, 2004, INT J PRIMATOL | 10.1023/B:IJOP.0000029127.31190.e9 | 39 | 2.05 | 1.05 |
| MCKENNEY EA, 2015, PLOS ONE | 10.1371/journal.pone.0124618 | 39 | 4.88 | 2.49 |
| PASTORINI J, 2001, AM J PRIMATOL | 10.1002/1098-2345(200101)53:1<1::AID-AJP1>3.0.CO;2-J | 38 | 1.73 | 1.49 |
plot(x = results, k = 10, pause = FALSE)
## Warning: Removed 2 rows containing missing values (position_stack).
## Warning: Removed 1 rows containing missing values (position_stack).## Warning: Removed 1 row(s) containing missing values (geom_path).
#Top-Authors’ Productivity over the Time:
topAU <- authorProdOverTime(M, k = 10, graph = TRUE)
#Chart of Paper per decade
G <- M %>%
mutate(Decade = as.numeric(PY) - as.numeric(PY) %% 10) %>%
group_by(Decade) %>%
summarize(val = n()) %>%
ungroup()
G$Decade = as.factor(G$Decade)
ggplot2::ggplot(G, aes(x = Decade, y = val)) +
geom_bar(stat = "identity", aes(fill = factor(Decade))) +
scale_x_discrete(labels = G %>% distinct(Decade) %>% mutate(Decade = paste0(Decade, "s")) %>% pull()) +
geom_text(aes(label = format(val, big.mark = ",")), size = 5, vjust =
-0.3) +
ggtitle('Number of Papers per Decade') +
theme(
panel.border = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.position = "none",
plot.title = element_text(face = "bold", size = 18, hjust = 0.5),
text = element_text(),
panel.background = element_rect(fill = "white"),
plot.background = element_rect(colour = NA),
axis.title = element_text(face = "bold", size = rel(1)),
axis.text = element_text(size = 16),
axis.line = element_line(colour = "black"),
panel.grid.major = element_line(colour = "#f0f0f0"),
panel.grid.minor = element_blank(),
axis.ticks = element_line(colour = "black"),
plot.margin = unit(c(10, 5, 5, 5), "mm"),
strip.background = element_rect(colour = "#f0f0f0", fill = "#f0f0f0"),
strip.text = element_text(face = "bold")
)
#Co-word analysis: cluster terms extracted from keywords, titles, or abstracts
NetMatrix <-
biblioNetwork(M,
analysis = "co-occurrences",
network = "keywords",
sep = ";")
netplot = networkPlot(
NetMatrix,
normalize = "association",
weighted = T,
n = 50,
Title = "Keyword Co-occurrences",
type = "auto",
cluster = "louvain",
community.repulsion = 0.15,
size = T,
edgesize = 7,
labelsize = 1,
remove.multiple = TRUE,
remove.isolates = T
)
#perform multiple correspondence analysis (MCA): identify clusters of documents that express common concepts
CS <-
conceptualStructure(
M,
field = "ID",
method = "MCA",
minDegree = 5,
clust = 4 ,
k.max = 5,
stemming = FALSE,
labelsize = 15
)## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
#trend topics by year
res <-
fieldByYear(
M,
field = "ID",
timespan = c(1978, 2022),
min.freq = 5,
n.items = 5,
graph = TRUE
)
#Thematic Map - starts from a co-occurrence keyword network to plot in a two-dimensional map the themes of a domain.
Map = thematicMap(
M,
field = "ID",
n = 55,
minfreq = 4,
stemming = TRUE,
size = 0.7,
n.labels = 3
)
plot(Map$map)
Clusters = Map$words[order(Map$words$Cluster, -Map$words$Occurrences), ]
CL <-
Clusters %>% group_by(.data$Cluster_Label) %>% top_n(5, .data$Occurrences)
CL## # A tibble: 27 x 5
## # Groups: Cluster_Label [4]
## Occurrences Words Cluster Color Cluster_Label
## <dbl> <chr> <dbl> <chr> <chr>
## 1 14 madagascar 1 #E41A1C80 madagascar
## 2 10 behavior 1 #E41A1C80 madagascar
## 3 10 evolution 1 #E41A1C80 madagascar
## 4 10 primates 1 #E41A1C80 madagascar
## 5 7 forest 1 #E41A1C80 madagascar
## 6 12 propithecus-verreauxi-coquereli 2 #377EB880 propithecus-verreauxi-coquereli
## 7 11 hapalemur-griseus 2 #377EB880 propithecus-verreauxi-coquereli
## 8 11 varecia-variegata 2 #377EB880 propithecus-verreauxi-coquereli
## 9 6 ring-tailed lemurs 2 #377EB880 propithecus-verreauxi-coquereli
## 10 5 gastrointestinal-tract 2 #377EB880 propithecus-verreauxi-coquereli
## # ... with 17 more rows# Keyword growth
topkw = KeywordGrowth(
M,
Tag = "ID",
sep = ";",
top = 15,
cdf = TRUE
)## Joining, by = "Tab"topkw$PRIMATES <- topkw$PRIMATES + topkw$PRIMATE
topkw$LEMURS <- topkw$LEMURS + topkw$LEMUR
topkw$`PROPITHECUS-VERREAUXI-COQUERELI` <-
topkw$`PROPITHECUS-VERREAUXI-COQUERELI` + topkw$VERREAUXI
topkw <- select(topkw,-PRIMATE)
topkw <- select(topkw,-LEMUR)
topkw <- select(topkw,-`RING-TAILED LEMURS`)
topkw <- select(topkw,-VERREAUXI)
topkw <- select(topkw,-`GASTROINTESTINAL-TRACT`)
topkw = rename(topkw, P.V.COQUERELI = `PROPITHECUS-VERREAUXI-COQUERELI`)
topkw <- subset(topkw, Year >= 1990)
DF = reshape::melt(topkw, id = 'Year') # reshape original data structure
alltopkw = KeywordGrowth(
allM,
Tag = "ID",
sep = ";",
top = 15,
cdf = TRUE
)## Joining, by = "Tab"alltopkw$PRIMATES <- alltopkw$PRIMATES + alltopkw$PRIMATE
alltopkw <- select(alltopkw,-PRIMATE)
alltopkw = rename(alltopkw, P.D.EDWARDSI = `PROPITHECUS-DIADEMA-EDWARDSI`)
alltopkw <- subset(alltopkw, Year >= 1990)
allDF = reshape::melt(alltopkw, id = 'Year') # reshape original data structure
lemurtopkw = KeywordGrowth(
lemurs,
Tag = "ID",
sep = ";",
top = 15,
cdf = TRUE
)## Joining, by = "Tab"lemurtopkw$PRIMATES <- lemurtopkw$PRIMATES + lemurtopkw$PRIMATE
lemurtopkw$LEMURS <- lemurtopkw$LEMURS + lemurtopkw$LEMUR
lemurtopkw <- select(lemurtopkw,-PRIMATE)
lemurtopkw <- select(lemurtopkw,-LEMUR)
lemurtopkw <- select(lemurtopkw,-POPULATION)
lemurtopkw <- select(lemurtopkw,-CONSERVATION)
lemurtopkw <- select(lemurtopkw,-`MICROCEBUS-MURINUS`)
lemurtopkw <- subset(lemurtopkw, Year >= 1990)
lemurDF = reshape::melt(lemurtopkw, id = 'Year') # reshape original data structure
update_geom_defaults("text", list(size = 2.8))
ggplot(NULL, aes(Year, value, group = variable)) +
geom_line(data = lemurDF, aes(color = "black")) +
geom_line(data = allDF, aes(color = "blue")) +
geom_line(data = DF, aes(color = "red")) +
scale_shape_manual(values = 1:15) +
scale_x_continuous(breaks = seq(1990, max(DF$Year), by = 10)) +
scale_y_continuous() +
labs(
y = "Count",
variable = "Keywords",
colour = "Search Term:",
title = "Keywords Usage Evolution Over Time"
) +
scale_color_manual(
labels = c("Lemur*", "Propithecus", "P. Coquereli"),
values = c("black", "blue", "red")
) +
facet_wrap(variable ~ ., ncol = 4, scales = "free") +
geom_text(
data = DF %>%
arrange(desc(Year)) %>%
group_by(variable) %>%
slice(1),
aes(label = value),
position = position_nudge(2),
hjust = 0.5,
show.legend = FALSE
) +
geom_text(
data = allDF %>%
arrange(desc(Year)) %>%
group_by(variable) %>%
slice(1),
aes(label = value),
position = position_nudge(2),
hjust = 0.5,
show.legend = FALSE
) +
geom_text(
data = lemurDF %>%
arrange(desc(Year)) %>%
group_by(variable) %>%
slice(1),
aes(label = value),
position = position_nudge(2),
hjust = 0.5,
show.legend = FALSE
) +
theme(
panel.border = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
plot.title = element_text(face = "bold", size = 12),
text = element_text(size = 10),
panel.background = element_rect(fill = "white"),
plot.background = element_rect(colour = NA),
axis.text = element_text(size = 10),
axis.line = element_line(colour = "black"),
panel.grid.major = element_line(colour = "#f0f0f0"),
panel.grid.minor = element_blank(),
axis.ticks = element_line(colour = "black"),
plot.margin = unit(c(5, 5, 5, 5), "mm"),
strip.background = element_rect(colour = "#f0f0f0", fill = "#f0f0f0"),
legend.position = "top",
legend.justification = "right",
legend.box.margin = margin(c(-27.5, 5, 5, 5)),
legend.text = element_text(size = 10),
strip.text = element_text(face = "bold")
)
# Three field plots
threeFieldsPlot(M, fields = c("JI", "AU", "ID"), n = c(10, 10, 25))#The summary statistics of the network; The main indices of centrality and prestige of vertices.
NetMatrix <-
biblioNetwork(M,
analysis = "co-occurrences",
network = "keywords",
sep = ";")
netstat <- networkStat(NetMatrix)
summary(netstat, k = 10)##
##
## Main statistics about the network
##
## Size 379
## Density 0.034
## Transitivity 0.399
## Diameter 6
## Degree Centralization 0.204
## Average path length 2.742
## #Thematic Evolution Analysis
nexus <-
thematicEvolution(
M,
field = "ID",
years = c(2000, 2010, 2020),
n = 100,
minFreq = 2
)
plotThematicEvolution(nexus$Nodes, nexus$Edges)# Create a historical citation network
histResults <- histNetwork(M, sep = ";")##
## WOS DB:
## Searching local citations (LCS) by reference items (SR) and DOIs...
##
## Analyzing 4700 reference items...
##
## Found 42 documents with no empty Local Citations (LCS)net <- histPlot(histResults,
n = 17,
size = 7,
labelsize = 5)
##
## Legend
##
## Label DOI Year LCS
## 1 EAGLEN RH, 1978, FOLIA PRIMATOL DOI 10.1159/000155864 10.1159/000155864 1978 4
## 2 KUBZDELA KS, 1992, AM J PRIMATOL DOI 10.1002/AJP.1350280206 10.1002/ajp.1350280206 1992 4
## 3 RAVOSA MJ, 1993, AM J PHYS ANTHROPOL DOI 10.1002/AJPA.1330920408 10.1002/ajpa.1330920408 1993 4
## 4 MAYOR MI, 2004, INT J PRIMATOL DOI 10.1023/B:IJOP.0000029127.31190.E9 10.1023/B:IJOP.0000029127.31190.e9 2004 7
## 5 HAYES RA, 2004, AM J PRIMATOL DOI 10.1002/AJP.20038 10.1002/ajp.20038 2004 2
## 6 CAMPBELL JL, 2004, AM J PRIMATOL DOI 10.1002/AJP.20081 10.1002/ajp.20081 2004 12
## 7 BASTIAN ML, 2007, INT J PRIMATOL DOI 10.1007/S10764-007-9115-Y 10.1007/s10764-007-9115-y 2007 2
## 8 SCORDATO ES, 2007, CHEM SENSES DOI 10.1093/CHEMSE/BJM018 10.1093/chemse/bjm018 2007 3
## 9 YAMASHITA N, 2008, INT J PRIMATOL DOI 10.1007/S10764-008-9232-2 10.1007/s10764-008-9232-2 2008 5
## 10 SIMMEN B, 2010, PLOS ONE DOI 10.1371/JOURNAL.PONE.0009860 10.1371/journal.pone.0009860 2010 2
## 11 FICHTEL C, 2011, INT J PRIMATOL DOI 10.1007/S10764-010-9472-9 10.1007/s10764-010-9472-9 2011 2
## 12 KUN-RODRIGUES C, 2014, AM J PRIMATOL DOI 10.1002/AJP.22243 10.1002/ajp.22243 2014 8
## 13 SALMONA J, 2014, ENDANGER SPECIES RES DOI 10.3354/ESR00622 10.3354/esr00622 2014 5
## 14 REA MS, 2014, AM J PHYS ANTHROPOL DOI 10.1002/AJPA.22409 10.1002/ajpa.22409 2014 2
## 15 GARDNER CJ, 2015, PRIMATES DOI 10.1007/S10329-015-0462-6 10.1007/s10329-015-0462-6 2015 3
## 16 GRANATOSKY MC, 2016, AM J PHYS ANTHROPOL DOI 10.1002/AJPA.22991 10.1002/ajpa.22991 2016 3
## 17 SATO H, 2016, INT J PRIMATOL DOI 10.1007/S10764-015-9877-6 10.1007/s10764-015-9877-6 2016 4
## 18 EPPLEY TM, 2016, AM J PHYS ANTHROPOL DOI 10.1002/AJPA.23034 10.1002/ajpa.23034 2016 3
## 19 BRAY J, 2017, AM J PRIMATOL DOI 10.1002/AJP.22648 10.1002/ajp.22648 2017 2
## GCS
## 1 11
## 2 24
## 3 75
## 4 39
## 5 41
## 6 31
## 7 6
## 8 83
## 9 22
## 10 35
## 11 19
## 12 21
## 13 11
## 14 8
## 15 8
## 16 21
## 17 25
## 18 14
## 19 10# biblioshiny()